Date: Thu Feb 27 13:12:41 2020
Scientist: Ran Yin
Sequencing (Waksman): Dibyendu Kumar
Statistics: Davit Sargsyan
Principal Investigator: Ah-Ng Kong
# Taxonomic Ranks:
# **K**ing **P**hillip **C**an n**O**t **F**ind **G**reen **S**ocks
# * Kingdom
# * Phylum
# * Class
# * Order
# * Family
# * Genus
# * Species
```r
# options(stringsAsFactors = FALSE,
# scipen = 999)
# # Increase mmemory size to 64 Gb----
# invisible(utils::memory.limit(65536))
# str(knitr::opts_chunk$get())
# # NOTE: the below does not work!
# knitr::opts_chunk$set(echo = FALSE,
# message = FALSE,
# warning = FALSE,
# error = FALSE)
# require(knitr)
# require(kableExtra)
require(phyloseq)
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Loading required package: phyloseq
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```r
```r
# require(shiny)
require(data.table)
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Loading required package: data.table data.table 1.12.2 using 18 threads (see ?getDTthreads). Latest news: r-datatable.com
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```r
```r
require(ggplot2)
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```r
```r
require(plotly)
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Loading required package: plotly
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```r
```r
require(DT)
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```r
```r
require(lmerTest)
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Loading required package: lmerTest Loading required package: lme4 Loading required package: Matrix
Attaching package: ‘lmerTest’
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```r
```r
source(\source/functions_may2019.R\)
# On Windows set multithread=FALSE----
mt <- TRUE
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# Introduction
C57BL/6 wild-type (WT) and Nrf-2 double-knock-out (KO -/-) mice were given 2-week microbiome stabilization process using AIN93M diet and 8 more weeks to treat with either AIN93M or AIN93M 5% PEITC diet. Fecal samples were collected weekly, immediately frozen in liquid nitrogen and stored at -80^o^C. Serum, cecal, colon epithelial and whole colon tissues at week 10 were also collected for further analyses. Baseline, week 1 and 4 fecal samples were selected for 16s rRNA sequencing.
This document examines results from the WT mice samples.
We will attampt to answer the following questions:
1. Did microbiome change over time?
2. Was microbiome affected by diet?
3. Was there a difference between the KO and WT?
4. If there was a change in microbiome composition, what functional changes did it carry? What are the essential functions of the bacteria affected by the treatment and how can this be shown in vivo (metabolites, inflammation markers, etc.)?
# Data preprocessing
## Raw Data
FastQ files were downloaded from [this Rutgers Box location](https://rutgers.app.box.com/folder/90143462291). A total of 144 files (2 per sample, pair-ended) and a pair of undetermined reads were downloaded.
## Script
This script (***nrf2ubiome_dada2_sep2019_v1.Rmd***) was developed using [DADA2 Pipeline Tutorial (1.12)](https://benjjneb.github.io/dada2/tutorial.html) with tips and tricks from the [University of Maryland Shool of Medicine Institute for Genome Sciences (IGS)](http://www.igs.umaryland.edu/) [Microbiome Analysis Workshop (April 8-11, 2019)](http://www.igs.umaryland.edu/education/wkshp_metagenome.php). The output of the DADA2 script (***data_may2019/ps_sep2019.RData***) is explored in this document.
# Meta data: sample description
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```r
# Load data----
# Counts
load("data_sep2019/ps_sep2019.RData")
# Taxonomy
load("data_sep2019/taxa.RData")
taxa <- data.table(seq16s = rownames(taxa),
taxa)
NOTE: correction to the meta-data! (11/15/2019)
correct_samples <- fread("data_sep2019/16s metadata Sep-2019.csv")
ps_sep2019@sam_data$DSS <- correct_samples$DSS
Samples
ps_sep2019@sam_data$Genotype_Week <- paste(ps_sep2019@sam_data$genotype,
ps_sep2019@sam_data$time,
sep = "_")
ps_sep2019@sam_data$ID <- factor(paste0(ps_sep2019@sam_data$mice_num,
ps_sep2019@sam_data$cage))
ps_sep2019@sam_data$TREATMENT <- paste0(ps_sep2019@sam_data$DSS,
ps_sep2019@sam_data$PEITC,
ps_sep2019@sam_data$cranberry)
ps_sep2019@sam_data$TREATMENT <- factor(ps_sep2019@sam_data$TREATMENT,
levels = c("000",
"100",
"110",
"101"),
labels = c("Naive",
"DSS",
"DSS+PEITC",
"DSS+Cranberry"))
samples <- ps_sep2019@sam_data
datatable(samples,
options = list(pageLength = nrow(samples)))
Prune data
The OTUs were mapped to Bacteria (96.07%), Eukaryota (2.95%) and Archea (0.03%) kingdoms, and 75 OTUs (0.95%) undefined.
The total of 7,867 unique sequences were found. Out of those, 7,558 were mapped to bacterial genomes.
dim(ps_sep2019@otu_table@.Data)
[1] 72 7867
# Remove OTU not mapped to Bacteria
ps0 <- subset_taxa(ps_sep2019,
Kingdom == "Bacteria")
dim(ps0@otu_table@.Data)
[1] 72 7558
Out of the 7,558 OTUs 7,247 belonged to 12 Phyla. 311 of the OTUs (or 4.11% of bacterial OTUs) could not be mapped to a phylum.
t2 <- data.table(table(tax_table(ps0)[, "Phylum"],
exclude = NULL))
t2$V1[is.na(t2$V1)] <- "Unknown"
setorder(t2, -N)
t2[, pct := N/sum(N)]
setorder(t2, -N)
colnames(t2) <- c("Phylum",
"Number of OTUs",
"Percent of OTUs")
datatable(t2,
rownames = FALSE,
caption = "Number of Bacterial OTUs by Phylum",
class = "cell-border stripe",
options = list(search = FALSE,
pageLength = nrow(t2))) %>%
formatCurrency(columns = 2,
currency = "",
mark = ",",
digits = 0) %>%
formatPercentage(columns = 3,
digits = 2)
OTU table (first 10 rows)
Total counts per sample (i.e. sequencing depth)
t1 <- colSums(otu[, 7:ncol(otu)])
t1 <- data.table(SAMPLE_NAME = names(t1),
Total = t1)
t2 <- data.table(SAMPLE_NAME = rownames(samples),
ID = samples$ID,
CAGE = samples$cage,
TREATMENT = samples$TREATMENT,
Genotype = samples$genotype,
WEEK = samples$time)
smpl <- merge(t1,
t2,
by = "SAMPLE_NAME")
p1 <- ggplot(smpl,
aes(x = SAMPLE_NAME,
y = Total,
fill = TREATMENT,
colour = WEEK)) +
facet_wrap(~ Genotype,
scale = "free_x") +
geom_bar(stat = "identity") +
scale_x_discrete("") +
scale_y_continuous("Number of Reads") +
scale_fill_discrete("Treatment") +
theme(axis.text.x = element_text(angle = 45,
hjust = 1))
ggplotly(p1)
Richness (Alpha diversity)
Shannon index (aka Shannon enthrophy) is calculated as:
H’ = -sum(1 to R)p(i)ln(p(i)) When there is exactly 1 type of data (e.g. a single species in the sample), H’=0. The opposite scenario is when there are R>1 species present in the sample in the exact same amounts and H’=ln(R).
Shannon’s diversity index was calculated for each sample and ploted over time using the 7,764 from the 13 Phylum above.
shannon.ndx <- estimate_richness(ps0,
measures = "Shannon")
shannon.ndx <- data.table(SAMPLE_NAME = rownames(shannon.ndx),
shannon.ndx)
smpl <- merge(smpl,
shannon.ndx,
by = "SAMPLE_NAME")
p1 <- ggplot(smpl,
aes(x = Total,
y = Shannon,
fill = Genotype,
shape = WEEK)) +
geom_point(size = 2) +
scale_shape_manual(breaks = unique(smpl$WEEK),
values = 21:23)
tiff(filename = "tmp/shannon_vs_depth.tiff",
height = 5,
width = 6,
units = "in",
res = 600,
compression = "lzw+p")
print(p1)
graphics.off()
ggplotly(p1)

Even though estimate_richness function does not adjust for the sequencing depth, there is no correlation between the index and the sample’s sequecing depth. Proceed with the comparison.
Shannon idex over time
p1 <- plot_richness(ps0,
x = "time",
measures = "Shannon") +
facet_wrap(~ genotype) +
geom_line(aes(group = ID),
color = "black") +
geom_point(aes(fill = TREATMENT),
shape = 21,
size = 3,
color = "black") +
scale_x_discrete("") +
theme(axis.text.x = element_text(angle = 30,
hjust = 1,
vjust = 1))
ggplotly(p = p1,
tooltip = c("ID",
"value"))
p1 <- p1 +
scale_fill_discrete("") +
theme(legend.position = "top")
tiff(filename = "tmp/shannon.tiff",
height = 4,
width = 5,
units = "in",
res = 600,
compression = "lzw+p")
print(p1)
graphics.off()
The plot above suggests that the largest differences in alpha diversity (as measured by Shannon’s index) are in genotype.
Test if the richness changed between the baseline and Week 8.
smpl$TREATMENT <- factor(smpl$TREATMENT,
levels = c("DSS",
"Naive",
"DSS+PEITC",
"DSS+Cranberry"))
tmp <- droplevels(smpl[WEEK != "week1"])
m1 <- lm(Shannon ~ WEEK*(TREATMENT + Genotype),
# offset = Total,
data = tmp)
summary(m1)
Call:
lm(formula = Shannon ~ WEEK * (TREATMENT + Genotype), data = tmp)
Residuals:
Min 1Q Median 3Q Max
-0.316186 -0.091027 0.007886 0.110704 0.293230
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.94987 0.07064 84.233 < 2e-16
WEEKweek8 0.01158 0.09989 0.116 0.9084
TREATMENTNaive 0.14581 0.08935 1.632 0.1109
TREATMENTDSS+PEITC -0.03923 0.08935 -0.439 0.6631
TREATMENTDSS+Cranberry -0.22582 0.08935 -2.527 0.0158
Genotypewidetype -0.54156 0.06318 -8.572 2.06e-10
WEEKweek8:TREATMENTNaive 0.01181 0.12636 0.093 0.9261
WEEKweek8:TREATMENTDSS+PEITC 0.01652 0.12636 0.131 0.8966
WEEKweek8:TREATMENTDSS+Cranberry 0.21535 0.12636 1.704 0.0965
WEEKweek8:Genotypewidetype 0.23085 0.08935 2.584 0.0137
(Intercept) ***
WEEKweek8
TREATMENTNaive
TREATMENTDSS+PEITC
TREATMENTDSS+Cranberry *
Genotypewidetype ***
WEEKweek8:TREATMENTNaive
WEEKweek8:TREATMENTDSS+PEITC
WEEKweek8:TREATMENTDSS+Cranberry .
WEEKweek8:Genotypewidetype *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1548 on 38 degrees of freedom
Multiple R-squared: 0.7845, Adjusted R-squared: 0.7335
F-statistic: 15.37 on 9 and 38 DF, p-value: 3.671e-10
m2 <- lmer(Shannon ~ WEEK*(TREATMENT + Genotype) + (1 | ID),
# offset = Total,
data = tmp)
summary(m2)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Shannon ~ WEEK * (TREATMENT + Genotype) + (1 | ID)
Data: tmp
REML criterion at convergence: -22.2
Scaled residuals:
Min 1Q Median 3Q Max
-1.53721 -0.47495 0.06753 0.44489 1.53874
Random effects:
Groups Name Variance Std.Dev.
ID (Intercept) 0.01259 0.1122
Residual 0.01136 0.1066
Number of obs: 48, groups: ID, 24
Fixed effects:
Estimate Std. Error df t value
(Intercept) 5.94987 0.07064 29.76837 84.233
WEEKweek8 0.01158 0.06879 19.00000 0.168
TREATMENTNaive 0.14581 0.08935 29.76837 1.632
TREATMENTDSS+PEITC -0.03923 0.08935 29.76837 -0.439
TREATMENTDSS+Cranberry -0.22582 0.08935 29.76837 -2.527
Genotypewidetype -0.54156 0.06318 29.76837 -8.572
WEEKweek8:TREATMENTNaive 0.01181 0.08701 19.00000 0.136
WEEKweek8:TREATMENTDSS+PEITC 0.01652 0.08701 19.00000 0.190
WEEKweek8:TREATMENTDSS+Cranberry 0.21535 0.08701 19.00000 2.475
WEEKweek8:Genotypewidetype 0.23085 0.06152 19.00000 3.752
Pr(>|t|)
(Intercept) < 2e-16 ***
WEEKweek8 0.86814
TREATMENTNaive 0.11322
TREATMENTDSS+PEITC 0.66379
TREATMENTDSS+Cranberry 0.01704 *
Genotypewidetype 1.55e-09 ***
WEEKweek8:TREATMENTNaive 0.89350
WEEKweek8:TREATMENTDSS+PEITC 0.85139
WEEKweek8:TREATMENTDSS+Cranberry 0.02291 *
WEEKweek8:Genotypewidetype 0.00135 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) WEEKw8 TREATMENTN TREATMENTDSS+P
WEEKweek8 -0.487
TREATMENTNv -0.632 0.308
TREATMENTDSS+P -0.632 0.308 0.500
TREATMENTDSS+C -0.632 0.308 0.500 0.500
Gentypwdtyp -0.447 0.218 0.000 0.000
WEEK8:TREATMENTN 0.308 -0.632 -0.487 -0.243
WEEK8:TREATMENTDSS+P 0.308 -0.632 -0.243 -0.487
WEEK8:TREATMENTDSS+C 0.308 -0.632 -0.243 -0.243
WEEKwk8:Gnt 0.218 -0.447 0.000 0.000
TREATMENTDSS+C Gntypw WEEK8:TREATMENTN
WEEKweek8
TREATMENTNv
TREATMENTDSS+P
TREATMENTDSS+C
Gentypwdtyp 0.000
WEEK8:TREATMENTN -0.243 0.000
WEEK8:TREATMENTDSS+P -0.243 0.000 0.500
WEEK8:TREATMENTDSS+C -0.487 0.000 0.500
WEEKwk8:Gnt 0.000 -0.487 0.000
WEEK8:TREATMENTDSS+P WEEK8:TREATMENTDSS+C
WEEKweek8
TREATMENTNv
TREATMENTDSS+P
TREATMENTDSS+C
Gentypwdtyp
WEEK8:TREATMENTN
WEEK8:TREATMENTDSS+P
WEEK8:TREATMENTDSS+C 0.500
WEEKwk8:Gnt 0.000 0.000
Calculate change in Shannon index from baseline
dd <- smpl
dd[, delta := Shannon - Shannon[WEEK == "baseline"],
by = ID]
dd$diff <- paste(dd$WEEK,
"-baseline",
sep = "")
dd <- dd[WEEK != "baseline",]
p1 <- ggplot(dd,
aes(x = TREATMENT,
y = delta,
fill = Genotype)) +
facet_wrap(~ diff) +
geom_hline(yintercept = 0,
linetype = "dashed") +
geom_point(position = position_dodge(0.3),
shape = 21,
size = 3) +
scale_y_continuous("Shannon Index Percent Change from Baseline") +
theme(axis.text.x = element_text(angle = 45,
hjust = 1))
print(p1)

dd$TREATMENT <- factor(dd$TREATMENT,
levels = c("DSS",
"Naive",
"DSS+PEITC",
"DSS+Cranberry"))
dd$Genotype <- factor(dd$Genotype,
levels = c("widetype",
"nrf2KO"))
m1 <- lm(delta ~ TREATMENT*Genotype,
data = dd)
summary(m1)
Call:
lm(formula = delta ~ TREATMENT * Genotype, data = dd)
Residuals:
Min 1Q Median 3Q Max
-0.40513 -0.09560 -0.02012 0.09568 0.35517
Coefficients:
Estimate Std. Error t value
(Intercept) 0.25142 0.07286 3.451
TREATMENTNaive -0.04426 0.10303 -0.430
TREATMENTDSS+PEITC -0.15777 0.10303 -1.531
TREATMENTDSS+Cranberry 0.04463 0.10303 0.433
Genotypenrf2KO -0.18412 0.10303 -1.787
TREATMENTNaive:Genotypenrf2KO -0.02851 0.14571 -0.196
TREATMENTDSS+PEITC:Genotypenrf2KO 0.24747 0.14571 1.698
TREATMENTDSS+Cranberry:Genotypenrf2KO 0.07927 0.14571 0.544
Pr(>|t|)
(Intercept) 0.00133 **
TREATMENTNaive 0.66985
TREATMENTDSS+PEITC 0.13358
TREATMENTDSS+Cranberry 0.66720
Genotypenrf2KO 0.08153 .
TREATMENTNaive:Genotypenrf2KO 0.84586
TREATMENTDSS+PEITC:Genotypenrf2KO 0.09721 .
TREATMENTDSS+Cranberry:Genotypenrf2KO 0.58946
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1785 on 40 degrees of freedom
Multiple R-squared: 0.249, Adjusted R-squared: 0.1176
F-statistic: 1.894 on 7 and 40 DF, p-value: 0.09608
# No significant interactions, proceed with 2-way analysis
m2 <- lm(delta ~ TREATMENT + Genotype,
data = dd)
summary(m2)
Call:
lm(formula = delta ~ TREATMENT + Genotype, data = dd)
Residuals:
Min 1Q Median 3Q Max
-0.49158 -0.09742 -0.01290 0.11101 0.35281
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.21414 0.05849 3.661 0.000683 ***
TREATMENTNaive -0.05851 0.07399 -0.791 0.433377
TREATMENTDSS+PEITC -0.03404 0.07399 -0.460 0.647813
TREATMENTDSS+Cranberry 0.08427 0.07399 1.139 0.261014
Genotypenrf2KO -0.10956 0.05232 -2.094 0.042176 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1812 on 43 degrees of freedom
Multiple R-squared: 0.1674, Adjusted R-squared: 0.08999
F-statistic: 2.162 on 4 and 43 DF, p-value: 0.08947
At Week 8 there was significantly smaller increase of alpha diversity from baseline in Nrf2 KO compared to WT, and in DSS+Cranberry compared to DSS only.
Load aminoacids
aa <- fread("data_sep2019/sep2019_aminoacids.csv")
aa <- aa[!is.na(ID), ]
aa$ID <- paste0(aa$ID,
aa$CAGE)
smpl1 <- unique(smpl[, c("ID",
"TREATMENT",
"Genotype")])
smpl1$ID <- as.character(smpl1$ID)
aa <- merge(smpl1,
aa,
by = "ID")
aa[, trt_week := paste(TREATMENT,
WEEK,
sep = "_")]
aa$trt_week <- factor(aa$trt_week,
levels = c("Naive_week2",
"Naive_week6",
"DSS_week2",
"DSS_week6",
"DSS+Cranberry_week2",
"DSS+Cranberry_week6" ,
"DSS+PEITC_week2",
"DSS+PEITC_week6"))
for (i in 8:(ncol(aa) - 1)) {
tmp <- aa[, c(1, 3, 7, ncol(aa), i), with = FALSE]
colnames(tmp)[5] <- "Y"
p1 <- ggplot(tmp,
aes(x = trt_week,
y = Y,
fill = Genotype,
group = ID)) +
geom_line(position = position_dodge(0.3)) +
geom_point(shape = 21,
size = 3,
position = position_dodge(0.3)) +
scale_x_discrete("") +
scale_y_continuous(colnames(aa)[i]) +
theme(axis.text.x = element_text(angle = 45,
hjust = 1))
# tiff(filename = paste0("tmp/",
# colnames(aa)[i],
# ".tiff"),
# height = 4,
# width = 5,
# units = "in",
# res = 600,
# compression = "lzw+p")
# print(p1)
# graphics.off()
print(p1)
}




















Aminoacid data PCA
dt_pca <- aa[, Alanine:glutamine]
m1 <- prcomp(dt_pca)
summary(m1)
Importance of components:
PC1 PC2 PC3 PC4 PC5 PC6
Standard deviation 4.8108 2.5833 1.78182 1.13495 0.93367 0.84949
Proportion of Variance 0.6276 0.1810 0.08609 0.03493 0.02364 0.01957
Cumulative Proportion 0.6276 0.8086 0.89466 0.92959 0.95323 0.97280
PC7 PC8 PC9 PC10 PC11 PC12
Standard deviation 0.62035 0.49052 0.35960 0.26786 0.22940 0.20322
Proportion of Variance 0.01044 0.00652 0.00351 0.00195 0.00143 0.00112
Cumulative Proportion 0.98323 0.98976 0.99326 0.99521 0.99663 0.99775
PC13 PC14 PC15 PC16 PC17 PC18
Standard deviation 0.1486 0.13250 0.11932 0.11154 0.08896 0.07274
Proportion of Variance 0.0006 0.00048 0.00039 0.00034 0.00021 0.00014
Cumulative Proportion 0.9983 0.99883 0.99922 0.99955 0.99977 0.99991
PC19 PC20
Standard deviation 0.05110 0.02565
Proportion of Variance 0.00007 0.00002
Cumulative Proportion 0.99998 1.00000
# Select PC-s to pliot (PC1 & PC2)
choices <- 1:2
# Scores, i.e. points (df.u)
dt.scr <- data.table(m1$x[, choices])
# Add grouping variable
dt.scr$grp <- aa$trt_week
dt.scr$TREATMENT <- aa$TREATMENT
dt.scr$WEEK <- aa$WEEK
dt.scr
# Loadings, i.e. arrows (df.v)
dt.rot <- as.data.frame(m1$rotation[, choices])
dt.rot$feat <- rownames(dt.rot)
dt.rot <- data.table(dt.rot)
dt.rot
dt.load <- melt.data.table(dt.rot,
id.vars = "feat",
measure.vars = 1:2,
variable.name = "pc",
value.name = "loading")
dt.load$feat <- factor(dt.load$feat,
levels = unique(dt.load$feat))
# Plot loadings
p0 <- ggplot(data = dt.load,
aes(x = feat,
y = loading)) +
facet_wrap(~ pc,
nrow = 2) +
geom_bar(stat = "identity") +
ggtitle("PC Loadings") +
theme(plot.title = element_text(hjust = 0.5),
axis.text.x = element_text(angle = 45,
hjust = 1))
tiff(filename = "tmp/pc.1.2_loadings.tiff",
height = 5,
width = 8,
units = 'in',
res = 300,
compression = "lzw+p")
print(p0)
graphics.off()
print(p0)

# Axis labels
u.axis.labs <- paste(colnames(dt.rot)[1:2],
sprintf('(%0.1f%% explained var.)',
100*m1$sdev[choices]^2/sum(m1$sdev^2)))
u.axis.labs
[1] "PC1 (62.8% explained var.)" "PC2 (18.1% explained var.)"
# Based on Figure p0, keep only a few variables with high loadings in PC1 and PC2----
# var.keep.ndx <- which(dt.rot$feat %in% c(...))
# Or select all
# var.keep.ndx <- 3:ncol(dt1)
# Use dt.rot[var.keep.ndx,] and dt.rot$feat[var.keep.ndx]
p1 <- ggplot(data = dt.rot,
aes(x = PC1,
y = PC2)) +
coord_equal() +
geom_point(data = dt.scr,
aes(fill = grp),
shape = 21,
size = 2,
alpha = 0.5) +
geom_segment(aes(x = 0,
y = 0,
xend = 10*PC1,
yend = 10*PC2),
arrow = arrow(length = unit(1/2, 'picas')),
size = 1.2,
color = "black") +
geom_text(aes(x = 11*PC1,
y = 11*PC2,
label = dt.rot$feat),
# size = 5,
hjust = 0.5) +
scale_x_continuous(u.axis.labs[1]) +
scale_y_continuous(u.axis.labs[2]) +
scale_fill_discrete(name = "Group") +
ggtitle("Biplot of Aminoacids") +
theme(plot.title = element_text(hjust = 0.5,
size = 20))
tiff(filename = "tmp/aminoacids_biplot.tiff",
height = 10,
width = 10,
units = 'in',
res = 300,
compression = "lzw+p")
print(p1)
graphics.off()
ggplotly(p1)

p2 <- ggplot(data = dt.rot,
aes(x = PC1,
y = PC2)) +
coord_equal() +
geom_point(data = dt.scr,
aes(fill = WEEK),
shape = 21,
size = 2,
alpha = 0.5) +
geom_segment(aes(x = 0,
y = 0,
xend = 10*PC1,
yend = 10*PC2),
arrow = arrow(length = unit(1/2, 'picas')),
size = 1.2,
color = "black") +
geom_text(aes(x = 11*PC1,
y = 11*PC2,
label = dt.rot$feat),
# size = 5,
hjust = 0.5) +
scale_x_continuous(u.axis.labs[1]) +
scale_y_continuous(u.axis.labs[2]) +
scale_fill_discrete(name = "Week") +
ggtitle("Biplot of Aminoacids") +
theme(plot.title = element_text(hjust = 0.5,
size = 20))
tiff(filename = "tmp/aminoacids_by_week_biplot.tiff",
height = 10,
width = 10,
units = 'in',
res = 300,
compression = "lzw+p")
print(p2)
graphics.off()
ggplotly(p2)

p2 <- ggplot(data = dt.rot,
aes(x = PC1,
y = PC2)) +
coord_equal() +
geom_point(data = dt.scr,
aes(fill = TREATMENT),
shape = 21,
size = 2,
alpha = 0.5) +
geom_segment(aes(x = 0,
y = 0,
xend = 10*PC1,
yend = 10*PC2),
arrow = arrow(length = unit(1/2, 'picas')),
size = 1.2,
color = "black") +
geom_text(aes(x = 11*PC1,
y = 11*PC2,
label = dt.rot$feat),
# size = 5,
hjust = 0.5) +
scale_x_continuous(u.axis.labs[1]) +
scale_y_continuous(u.axis.labs[2]) +
scale_fill_discrete(name = "Treatment") +
ggtitle("Biplot of Aminoacids") +
theme(plot.title = element_text(hjust = 0.5,
size = 20))
tiff(filename = "tmp/aminoacids_by_trt_biplot.tiff",
height = 10,
width = 10,
units = 'in',
res = 300,
compression = "lzw+p")
print(p2)
graphics.off()
ggplotly(p2)

Remove unmapped OTUs
The 311 unmapped OTUs were removed from further analysis (with 7,247 OTUs left).
ps1 <- subset_taxa(ps0,
!is.na(Phylum))
dim(ps1@otu_table@.Data)
[1] 72 7247
Counts at Phylum level
Relative abundance (%) at Phylum level
Remove phyla with relative abundance of >= 1% in less than 10% of samples.
t1 <- data.table(Phylum = ra_p$Phylum,
`Number of Samples` = rowSums(ra_p[, 2:ncol(ra_p)] >= 0.01))
t1$`Percent Samples` <- t1$`Number of Samples`/72
setorder(t1, -`Number of Samples`)
datatable(t1,
rownames = FALSE,
caption = "Taxonomic count table",
class = "cell-border stripe",
options = list(search = FALSE,
pageLength = nrow(t1))) %>%
formatPercentage(columns = 3,
digits = 1)
We will remove Chlamydiae from this analysis.
XX OTUs, down from YY OTUs in the previous table.
Relative Abundance in Samples at Different Taxonomic Ranks
1. Class
mu$Trt_Genotype <- factor(paste(mu$Treatment,
mu$Genotype,
sep = "_"))
p0 <- ggplot(mu,
aes(x = Week,
y = x,
group = Trt_Genotype)) +
facet_wrap(~ Class,
scale = "free_y") +
geom_line(position = position_dodge(0.3)) +
geom_point(aes(fill = Trt_Genotype),
shape = 21,
size = 2,
alpha = 0.5,
position = position_dodge(0.3)) +
scale_x_discrete("") +
scale_y_continuous("Relative Abundance (%)") +
theme(legend.position = "top",
axis.text.x = element_text(angle = 45,
hjust = 1))
tiff(filename = "tmp/wt_class_over_time.tiff",
height = 5,
width = 7,
units = "in",
res = 600,
compression = "lzw+p")
print(p0)
graphics.off()
print(p0)
p1 <- ggplot(mu,
aes(x = x,
y = Class,
color = Trt_Genotype,
shape = Week)) +
geom_point(size = 3,
alpha = 0.5) +
geom_vline(xintercept = 1,
linetype = "dashed") +
scale_x_continuous("Relative Abundance (%)") +
theme(legend.position = "top")
tiff(filename = "tmp/wt_class_ra.tiff",
height = 4,
width = 7,
units = "in",
res = 600,
compression = "lzw+p")
print(p1)
graphics.off()
ggplotly(p1)
2. Order
mu$Trt_Genotype <- factor(paste(mu$Treatment,
mu$Genotype,
sep = "_"))
p0 <- ggplot(mu,
aes(x = Week,
y = x,
group = Trt_Genotype)) +
facet_wrap(~ Order,
scale = "free_y") +
geom_line(position = position_dodge(0.3)) +
geom_point(aes(fill = Trt_Genotype),
shape = 21,
size = 2,
alpha = 0.5,
position = position_dodge(0.3)) +
scale_x_discrete("") +
scale_y_continuous("Relative Abundance (%)") +
theme(legend.position = "top",
axis.text.x = element_text(angle = 45,
hjust = 1))
tiff(filename = "tmp/wt_Order_over_time.tiff",
height = 5,
width = 7,
units = "in",
res = 600,
compression = "lzw+p")
print(p0)
graphics.off()
print(p0)
p1 <- ggplot(mu,
aes(x = x,
y = Order,
color = Trt_Genotype,
shape = Week)) +
geom_point(size = 3,
alpha = 0.5) +
geom_vline(xintercept = 1,
linetype = "dashed") +
scale_x_continuous("Relative Abundance (%)") +
theme(legend.position = "top")
tiff(filename = "tmp/wt_Order_ra.tiff",
height = 4,
width = 7,
units = "in",
res = 600,
compression = "lzw+p")
print(p1)
graphics.off()
ggplotly(p1)
3. Family
NOTE: only the first 24 families had large enough counts - ploting only them.
mu$Trt_Genotype <- factor(paste(mu$Treatment,
mu$Genotype,
sep = "_"))
mu1 <- droplevels(mu[Family %in% levels(mu$Family)[nlevels(mu$Family):(nlevels(mu$Family) - 24)], ])
p0 <- ggplot(mu1,
aes(x = Week,
y = x,
group = Trt_Genotype)) +
facet_wrap(~ Family,
scale = "free_y") +
geom_line(position = position_dodge(0.3)) +
geom_point(aes(fill = Trt_Genotype),
shape = 21,
size = 2,
alpha = 0.5,
position = position_dodge(0.3)) +
scale_x_discrete("") +
scale_y_continuous("Relative Abundance (%)") +
theme(legend.position = "top",
axis.text.x = element_text(angle = 45,
hjust = 1))
tiff(filename = "tmp/wt_Family_over_time.tiff",
height = 7,
width = 9,
units = "in",
res = 600,
compression = "lzw+p")
print(p0)
graphics.off()
print(p0)
p1 <- ggplot(mu1,
aes(x = x,
y = Family,
color = Trt_Genotype,
shape = Week)) +
geom_point(size = 3,
alpha = 0.5) +
geom_vline(xintercept = 1,
linetype = "dashed") +
scale_x_continuous("Relative Abundance (%)") +
theme(legend.position = "top")
tiff(filename = "tmp/wt_Family_ra.tiff",
height = 4,
width = 7,
units = "in",
res = 600,
compression = "lzw+p")
print(p1)
graphics.off()
ggplotly(p1)
4. Genus
mu$Trt_Genotype <- factor(paste(mu$Treatment,
mu$Genotype,
sep = "_"))
mu1 <- droplevels(mu[Genus %in% levels(mu$Genus)[nlevels(mu$Genus):(nlevels(mu$Genus) - 35)], ])
p0 <- ggplot(mu1,
aes(x = Week,
y = x,
group = Trt_Genotype)) +
facet_wrap(~ Genus,
scale = "free_y") +
geom_line(position = position_dodge(0.3)) +
geom_point(aes(fill = Trt_Genotype),
shape = 21,
size = 2,
alpha = 0.5,
position = position_dodge(0.3)) +
scale_x_discrete("") +
scale_y_continuous("Relative Abundance (%)") +
theme(legend.position = "top",
axis.text.x = element_text(angle = 45,
hjust = 1))
tiff(filename = "tmp/wt_Genus_over_time.tiff",
height = 9,
width = 12,
units = "in",
res = 600,
compression = "lzw+p")
print(p0)
graphics.off()
print(p0)
p1 <- ggplot(mu1,
aes(x = x,
y = Genus,
color = Trt_Genotype,
shape = Week)) +
geom_point(size = 3,
alpha = 0.5) +
geom_vline(xintercept = 1,
linetype = "dashed") +
scale_x_continuous("Relative Abundance (%)") +
theme(legend.position = "top")
tiff(filename = "tmp/wt_Genus_ra.tiff",
height = 9,
width = 9,
units = "in",
res = 600,
compression = "lzw+p")
print(p1)
graphics.off()
ggplotly(p1)
---
title: "Data Visualization of WT and Nrf2 KO (-/-) BL6 PEITC or Cranberry Treated Mice 16S Microbiome Data Analysis, September 2019 Batch"
output: 
  html_notebook:
    toc: yes
    toc_float: yes
    number_sections: yes
    code_folding: hide
---
Date: `r date()`     
Scientist: [Ran Yin](mailto:ry147@scarletmail.rutgers.edu)      
Sequencing (Waksman): [Dibyendu Kumar](mailto:dk@waksman.rutgers.edu)      
Statistics: [Davit Sargsyan](mailto:sargdavid@gmail.com)      
Principal Investigator: [Ah-Ng Kong](mailto:kongt@pharmacy.rutgers.edu) 

```{}
# Taxonomic Ranks:
# **K**ing **P**hillip **C**an n**O**t **F**ind **G**reen **S**ocks
# * Kingdom                
# * Phylum                    
# * Class                   
# * Order                   
# * Family     
# * Genus     
# * Species  
```

```{r setup}
# options(stringsAsFactors = FALSE,
#         scipen = 999)

# # Increase mmemory size to 64 Gb----
# invisible(utils::memory.limit(65536))


# str(knitr::opts_chunk$get())
# # NOTE: the below does not work!
# knitr::opts_chunk$set(echo = FALSE, 
#                       message = FALSE,
#                       warning = FALSE,
#                       error = FALSE)

# require(knitr)
# require(kableExtra)
require(phyloseq)
# require(shiny)

require(data.table)
require(ggplot2)
require(plotly)
require(DT)
require(lmerTest)

source("source/functions_may2019.R")

# On Windows set multithread=FALSE----
mt <- TRUE
```

# Introduction
C57BL/6 wild-type (WT) and Nrf-2 double-knock-out (KO -/-) mice were given 2-week microbiome stabilization process using AIN93M diet and 8 more weeks to treat with either AIN93M or AIN93M 5% PEITC diet. Fecal samples were collected weekly, immediately frozen in liquid nitrogen and stored at -80^o^C. Serum, cecal, colon epithelial and whole colon tissues at week 10 were also collected for further analyses. Baseline, week 1 and 4 fecal samples were selected for 16s rRNA sequencing.  
  
This document examines results from the WT mice samples.  
  
We will attampt to answer the following questions:  
1. Did microbiome change over time?  
2. Was microbiome affected by diet?  
3. Was there a difference between the KO and WT?  
4. If there was a change in microbiome composition, what functional changes did it carry? What are the essential functions of the bacteria affected by the treatment and how can this be shown in vivo (metabolites, inflammation markers, etc.)?

# Data preprocessing
## Raw Data 
FastQ files were downloaded from [this Rutgers Box location](https://rutgers.app.box.com/folder/90143462291). A total of 144 files (2 per sample, pair-ended) and a pair of undetermined reads were downloaded. 

## Script
This script (***nrf2ubiome_dada2_sep2019_v1.Rmd***) was developed using [DADA2 Pipeline Tutorial (1.12)](https://benjjneb.github.io/dada2/tutorial.html) with tips and tricks from the [University of Maryland Shool of Medicine Institute for Genome Sciences (IGS)](http://www.igs.umaryland.edu/) [Microbiome Analysis Workshop (April 8-11, 2019)](http://www.igs.umaryland.edu/education/wkshp_metagenome.php). The output of the DADA2 script (***data_may2019/ps_sep2019.RData***) is explored in this document.

# Meta data: sample description
```{r data}
# Load data----
# Counts
load("data_sep2019/ps_sep2019.RData")

# Taxonomy
load("data_sep2019/taxa.RData")
taxa <- data.table(seq16s = rownames(taxa),
                   taxa)
```

**NOTE: correction to the meta-data!** (11/15/2019)
```{r correct_meta_data}
correct_samples <- fread("data_sep2019/16s metadata Sep-2019.csv")
ps_sep2019@sam_data$DSS <- correct_samples$DSS
```

# Samples
```{r samples}
ps_sep2019@sam_data$Genotype_Week <- paste(ps_sep2019@sam_data$genotype,
                                           ps_sep2019@sam_data$time,
                                           sep = "_")
ps_sep2019@sam_data$ID <- factor(paste0(ps_sep2019@sam_data$mice_num,
                                        ps_sep2019@sam_data$cage))

ps_sep2019@sam_data$TREATMENT <- paste0(ps_sep2019@sam_data$DSS,
                                        ps_sep2019@sam_data$PEITC,
                                        ps_sep2019@sam_data$cranberry)
ps_sep2019@sam_data$TREATMENT <- factor(ps_sep2019@sam_data$TREATMENT,
                                        levels = c("000",
                                                   "100",
                                                   "110",
                                                   "101"),
                                        labels = c("Naive",
                                                   "DSS",
                                                   "DSS+PEITC",
                                                   "DSS+Cranberry"))

samples <- ps_sep2019@sam_data
datatable(samples,
          options = list(pageLength = nrow(samples)))
```

# Prune data
The OTUs were mapped to Bacteria (96.07%), Eukaryota (2.95%) and Archea (0.03%) kingdoms, and  75 OTUs (0.95%) undefined. 

```{r check_mapping_kingdom, warning = FALSE, echo = FALSE, message = FALSE}
t1 <- data.table(table(tax_table(ps_sep2019)[, "Kingdom"],
                       exclude = NULL))
t1$V1[is.na(t1$V1)] <- "Unknown"

t1[, pct := N/sum(N)]
setorder(t1, -N)

colnames(t1) <- c("Kingdom",
                  "Number of OTUs",
                  "Percent of OTUs")
datatable(t1,
          rownames = FALSE,
          caption = "Number of OTUs by Kingdom",
          class = "cell-border stripe",
          options = list(search = FALSE,
                         pageLength = nrow(t1))) %>%
  formatCurrency(columns = 2,
                 currency = "",
                 mark = ",",
                 digits = 0) %>%
  formatPercentage(columns = 3,
                   digits = 2)
```

The total of 7,867 unique sequences were found. Out of those, 7,558 were mapped to bacterial genomes. 

```{r keep_bacteria}
dim(ps_sep2019@otu_table@.Data)

# Remove OTU not mapped to Bacteria
ps0 <- subset_taxa(ps_sep2019, 
                   Kingdom == "Bacteria")
dim(ps0@otu_table@.Data)
```
  
Out of the 7,558 OTUs 7,247 belonged to 12 Phyla. 311 of the OTUs (or 4.11% of bacterial OTUs) could not be mapped to a phylum.

```{r phylum_mapping}
t2 <- data.table(table(tax_table(ps0)[, "Phylum"],
                                  exclude = NULL))
t2$V1[is.na(t2$V1)] <- "Unknown"
setorder(t2, -N)
t2[, pct := N/sum(N)]
setorder(t2, -N)

colnames(t2) <- c("Phylum",
                  "Number of OTUs",
                  "Percent of OTUs")

datatable(t2,
          rownames = FALSE,
          caption = "Number of Bacterial OTUs by Phylum",
          class = "cell-border stripe",
          options = list(search = FALSE,
                         pageLength = nrow(t2))) %>%
  formatCurrency(columns = 2,
                 currency = "",
                 mark = ",",
                 digits = 0) %>%
  formatPercentage(columns = 3,
                   digits = 2)
```

# OTU table (first 10 rows)
```{r otu_table, warning=FALSE,echo=FALSE,message=FALSE}
otu <- data.table(ps0@tax_table@.Data,
                  t(ps0@otu_table@.Data))
datatable(head(otu, 10),
          rownames = FALSE,
          caption = "Taxonomic  count table",
          class = "cell-border stripe",
          options = list(search = FALSE,
                         pageLength = 10)) %>%
  formatCurrency(columns = 7:36,
                 currency = "",
                 mark = ",",
                 digits = 0)
```

# Total counts per sample (i.e. sequencing depth)
```{r seq_depth, fig.width = 10,fig.height = 5}
t1 <- colSums(otu[, 7:ncol(otu)])
t1 <- data.table(SAMPLE_NAME = names(t1),
                 Total = t1)

t2 <- data.table(SAMPLE_NAME = rownames(samples),
                 ID = samples$ID,
                 CAGE = samples$cage,
                 TREATMENT = samples$TREATMENT,
                 Genotype = samples$genotype,
                 WEEK = samples$time)

smpl <- merge(t1,
              t2,
              by = "SAMPLE_NAME")

p1 <- ggplot(smpl,
             aes(x = SAMPLE_NAME,
                 y = Total,
                 fill = TREATMENT,
                 colour = WEEK)) +
  facet_wrap(~ Genotype,
             scale = "free_x") +
  geom_bar(stat = "identity") +
  scale_x_discrete("") +
  scale_y_continuous("Number of Reads") +
  scale_fill_discrete("Treatment") +
  theme(axis.text.x = element_text(angle = 45,
                                   hjust = 1)) 
ggplotly(p1)
```

# Richness (Alpha diversity)
Shannon index (aka Shannon enthrophy) is calculated as:  
H' = -sum(1 to R)p(i)ln(p(i)) 
When there is exactly 1 type of data (e.g. a single species in the sample), H'=0. The opposite scenario is when there are R>1 species present in the sample in the exact same amounts and H'=ln(R).  
  
Shannon's diversity index was calculated for each sample and ploted over time using the 7,764 from the 13 Phylum above.
  
```{r shannon_vs_depth, fig.height = 5, fig.width = 6}
shannon.ndx <- estimate_richness(ps0,
                                 measures = "Shannon")

shannon.ndx <- data.table(SAMPLE_NAME = rownames(shannon.ndx),
                          shannon.ndx)

smpl <- merge(smpl,
              shannon.ndx,
              by = "SAMPLE_NAME")

p1 <- ggplot(smpl,
             aes(x = Total,
                 y = Shannon,
                 fill = Genotype,
                 shape = WEEK)) +
  geom_point(size = 2) +
  scale_shape_manual(breaks = unique(smpl$WEEK),
                     values = 21:23)

tiff(filename = "tmp/shannon_vs_depth.tiff",
     height = 5,
     width = 6,
     units = "in",
     res = 600,
     compression = "lzw+p")
print(p1)
graphics.off()

ggplotly(p1)
```

Even though ***estimate_richness*** function does not adjust for the sequencing depth, there is no correlation between the index and the sample's sequecing depth. Proceed with the comparison.

# Shannon idex over time
```{r richness, fig.width = 4, fig.height = 5}
p1 <- plot_richness(ps0,
                    x = "time", 
                    measures = "Shannon") +
  facet_wrap(~ genotype) +
  geom_line(aes(group = ID),
            color = "black") +
  geom_point(aes(fill = TREATMENT),
             shape = 21,
             size = 3,
             color = "black") +
  scale_x_discrete("") +
  theme(axis.text.x = element_text(angle = 30,
                                   hjust = 1,
                                   vjust = 1))

ggplotly(p = p1,
         tooltip = c("ID",
                     "value"))

p1 <- p1 + 
  scale_fill_discrete("") +
  theme(legend.position = "top")

tiff(filename = "tmp/shannon.tiff",
     height = 4,
     width = 5,
     units = "in",
     res = 600,
     compression = "lzw+p")
print(p1)
graphics.off()
```

The plot above suggests that the largest differences in alpha diversity (as measured by Shannon's index) are in genotype.
  
Test if the richness changed between the baseline and Week 8.  
  
```{r lm_richness}
smpl$TREATMENT <- factor(smpl$TREATMENT,
                         levels = c("DSS",
                                    "Naive",
                                    "DSS+PEITC",
                                    "DSS+Cranberry"))

tmp <- droplevels(smpl[WEEK != "week1"])

m1 <- lm(Shannon  ~ WEEK*(TREATMENT + Genotype),
         # offset = Total,
         data = tmp)
summary(m1)
```
  
```{r lmer_richness}
m2 <- lmer(Shannon  ~ WEEK*(TREATMENT + Genotype) + (1 | ID),
           # offset = Total,
           data = tmp)
summary(m2)
```

# Calculate change in Shannon index from baseline
```{r delta_shannon, fig.width = 7, fig.height = 5}
dd <- smpl
dd[, delta := Shannon - Shannon[WEEK == "baseline"],
   by = ID]
dd$diff <- paste(dd$WEEK,
                 "-baseline",
                 sep = "")

dd <- dd[WEEK != "baseline",]

p1 <- ggplot(dd,
             aes(x = TREATMENT,
                 y = delta,
                 fill = Genotype)) +
  facet_wrap(~ diff) +
  geom_hline(yintercept = 0,
             linetype = "dashed") +
  geom_point(position = position_dodge(0.3),
             shape = 21,
             size = 3) +
  scale_y_continuous("Shannon Index Percent Change from Baseline") +
  theme(axis.text.x = element_text(angle = 45,
                                   hjust = 1))
print(p1)

dd$TREATMENT <- factor(dd$TREATMENT,
                        levels = c("DSS",
                                   "Naive",
                                   "DSS+PEITC",
                                   "DSS+Cranberry"))
dd$Genotype <- factor(dd$Genotype,
                       levels = c("widetype",
                                  "nrf2KO"))

m1 <- lm(delta ~ TREATMENT*Genotype,
         data = dd)
summary(m1)

# No significant interactions, proceed with 2-way analysis
m2 <- lm(delta ~ TREATMENT + Genotype,
         data = dd)
summary(m2)
```

At Week 8 there was significantly smaller increase of alpha diversity from baseline in Nrf2 KO compared to WT, and in DSS+Cranberry compared to DSS only.

# Load aminoacids
```{r aminoacids_data}
aa <- fread("data_sep2019/sep2019_aminoacids.csv")

aa <- aa[!is.na(ID), ]
aa$ID <- paste0(aa$ID,
                aa$CAGE)

smpl1 <- unique(smpl[, c("ID",
                            "TREATMENT",
                            "Genotype")])
smpl1$ID <- as.character(smpl1$ID)
aa <- merge(smpl1,
            aa,
            by = "ID")
aa[, trt_week := paste(TREATMENT,
                       WEEK,
                       sep = "_")]
aa$trt_week <- factor(aa$trt_week,
                      levels = c("Naive_week2",
                                 "Naive_week6",
                                 "DSS_week2",
                                 "DSS_week6",
                                 "DSS+Cranberry_week2",
                                 "DSS+Cranberry_week6" ,
                                 "DSS+PEITC_week2",
                                 "DSS+PEITC_week6"))
```

```{r aminoacids_plots, fig.height = 4, fig.width = 5}
for (i in 8:(ncol(aa) - 1)) {
  tmp <- aa[, c(1, 3, 7, ncol(aa), i), with = FALSE]
  colnames(tmp)[5] <- "Y"
  p1 <- ggplot(tmp,
               aes(x = trt_week,
                   y = Y,
                   fill = Genotype,
                   group = ID)) +
    geom_line(position = position_dodge(0.3)) +
    geom_point(shape = 21,
               size = 3,
               position = position_dodge(0.3)) +
    scale_x_discrete("") +
    scale_y_continuous(colnames(aa)[i]) +
    theme(axis.text.x = element_text(angle = 45,
                                     hjust = 1))
  # tiff(filename = paste0("tmp/",
  #                        colnames(aa)[i],
  #                        ".tiff"),
  #      height = 4,
  #      width = 5,
  #      units = "in",
  #      res = 600,
  #      compression = "lzw+p")
  # print(p1)
  # graphics.off()
  
  print(p1)
}
```

# Aminoacid data PCA
```{r aminoacids_pca}
dt_pca <- aa[, Alanine:glutamine]

m1 <- prcomp(dt_pca)
summary(m1)


# Select PC-s to pliot (PC1 & PC2)
choices <- 1:2
# Scores, i.e. points (df.u)
dt.scr <- data.table(m1$x[, choices])
# Add grouping variable
dt.scr$grp <- aa$trt_week
dt.scr$TREATMENT <- aa$TREATMENT
dt.scr$WEEK <- aa$WEEK
dt.scr

# Loadings, i.e. arrows (df.v)
dt.rot <- as.data.frame(m1$rotation[, choices])
dt.rot$feat <- rownames(dt.rot)
dt.rot <- data.table(dt.rot)
dt.rot

dt.load <- melt.data.table(dt.rot,
                           id.vars = "feat",
                           measure.vars = 1:2,
                           variable.name = "pc",
                           value.name = "loading")
dt.load$feat <- factor(dt.load$feat,
                       levels = unique(dt.load$feat))
# Plot loadings
p0 <- ggplot(data = dt.load,
             aes(x = feat,
                 y = loading)) +
  facet_wrap(~ pc,
             nrow = 2) +
  geom_bar(stat = "identity") +
  ggtitle("PC Loadings") +
  theme(plot.title = element_text(hjust = 0.5),
        axis.text.x = element_text(angle = 45,
                                   hjust = 1))
tiff(filename = "tmp/pc.1.2_loadings.tiff",
     height = 5,
     width = 8,
     units = 'in',
     res = 300,
     compression = "lzw+p")
print(p0)
graphics.off()

print(p0)
```

```{r aminoacids_pca_axes}
# Axis labels
u.axis.labs <- paste(colnames(dt.rot)[1:2], 
                     sprintf('(%0.1f%% explained var.)', 
                             100*m1$sdev[choices]^2/sum(m1$sdev^2)))
u.axis.labs
```

```{r aminoacids_biplot, fig.height = 10, fig.width = 10}
# Based on Figure p0, keep only a few variables with high loadings in PC1 and PC2----
# var.keep.ndx <- which(dt.rot$feat %in% c(...))
# Or select all
# var.keep.ndx <- 3:ncol(dt1)
# Use dt.rot[var.keep.ndx,] and dt.rot$feat[var.keep.ndx]
p1 <- ggplot(data = dt.rot,
             aes(x = PC1,
                 y = PC2)) +
  coord_equal() +
  geom_point(data = dt.scr,
             aes(fill = grp),
             shape = 21,
             size = 2,
             alpha = 0.5) +
  geom_segment(aes(x = 0,
                   y = 0,
                   xend = 10*PC1,
                   yend = 10*PC2),
               arrow = arrow(length = unit(1/2, 'picas')),
               size = 1.2, 
               color = "black") +
  geom_text(aes(x = 11*PC1,
                y = 11*PC2,
                label = dt.rot$feat),
            # size = 5,
            hjust = 0.5) +
  scale_x_continuous(u.axis.labs[1]) +
  scale_y_continuous(u.axis.labs[2]) +
  scale_fill_discrete(name = "Group") +
  ggtitle("Biplot of Aminoacids") +
  theme(plot.title = element_text(hjust = 0.5,
                                  size = 20))
tiff(filename = "tmp/aminoacids_biplot.tiff",
     height = 10,
     width = 10,
     units = 'in',
     res = 300,
     compression = "lzw+p")
print(p1)
graphics.off()

ggplotly(p1)
```

```{r aminoacids_biplot_by_week, fig.height = 10, fig.width = 10}
p2 <- ggplot(data = dt.rot,
             aes(x = PC1,
                 y = PC2)) +
  coord_equal() +
  geom_point(data = dt.scr,
             aes(fill = WEEK),
             shape = 21,
             size = 2,
             alpha = 0.5) +
  geom_segment(aes(x = 0,
                   y = 0,
                   xend = 10*PC1,
                   yend = 10*PC2),
               arrow = arrow(length = unit(1/2, 'picas')),
               size = 1.2, 
               color = "black") +
  geom_text(aes(x = 11*PC1,
                y = 11*PC2,
                label = dt.rot$feat),
            # size = 5,
            hjust = 0.5) +
  scale_x_continuous(u.axis.labs[1]) +
  scale_y_continuous(u.axis.labs[2]) +
  scale_fill_discrete(name = "Week") +
  ggtitle("Biplot of Aminoacids") +
  theme(plot.title = element_text(hjust = 0.5,
                                  size = 20))
tiff(filename = "tmp/aminoacids_by_week_biplot.tiff",
     height = 10,
     width = 10,
     units = 'in',
     res = 300,
     compression = "lzw+p")
print(p2)
graphics.off()

ggplotly(p2)
```

```{r aminoacids_biplot_by_trt, fig.height = 10, fig.width = 10}
p2 <- ggplot(data = dt.rot,
             aes(x = PC1,
                 y = PC2)) +
  coord_equal() +
  geom_point(data = dt.scr,
             aes(fill = TREATMENT),
             shape = 21,
             size = 2,
             alpha = 0.5) +
  geom_segment(aes(x = 0,
                   y = 0,
                   xend = 10*PC1,
                   yend = 10*PC2),
               arrow = arrow(length = unit(1/2, 'picas')),
               size = 1.2, 
               color = "black") +
  geom_text(aes(x = 11*PC1,
                y = 11*PC2,
                label = dt.rot$feat),
            # size = 5,
            hjust = 0.5) +
  scale_x_continuous(u.axis.labs[1]) +
  scale_y_continuous(u.axis.labs[2]) +
  scale_fill_discrete(name = "Treatment") +
  ggtitle("Biplot of Aminoacids") +
  theme(plot.title = element_text(hjust = 0.5,
                                  size = 20))
tiff(filename = "tmp/aminoacids_by_trt_biplot.tiff",
     height = 10,
     width = 10,
     units = 'in',
     res = 300,
     compression = "lzw+p")
print(p2)
graphics.off()

ggplotly(p2)
```
# Remove unmapped OTUs
The 311 unmapped OTUs were removed from further analysis (with 7,247 OTUs left).
```{r remove_unmapped_otu_phylum}
ps1 <- subset_taxa(ps0, 
                   !is.na(Phylum))
dim(ps1@otu_table@.Data)
```

# Counts at Phylum level
```{r counts_p, warning=FALSE,echo=FALSE,message=FALSE}
counts_p <- counts_by_tax_rank(dt1 = otu,
                               aggr_by = "Phylum")
setorder(counts_p, -`190919-01`)
datatable(counts_p,
          rownames = FALSE,
          caption = "Taxonomic  count table",
          class = "cell-border stripe",
          options = list(search = FALSE,
                         pageLength = nrow(counts_p))) %>%
  formatCurrency(columns = 2:ncol(counts_p),
                 currency = "",
                 mark = ",",
                 digits = 0)
```

# Relative abundance (%) at Phylum level
```{r ra_p, warning=FALSE,echo=FALSE,message=FALSE}
ra_p <- ra_by_tax_rank(counts = counts_p,
                       pct = FALSE,
                       digit = 4)

datatable(ra_p,
          rownames = FALSE,
          caption = "Taxonomic  count table",
          class = "cell-border stripe",
          options = list(search = FALSE,
                         pageLength = nrow(ra_p))) %>%
  formatPercentage(columns = 2:ncol(counts_p),
                   digits = 2)
```

Remove phyla with relative abundance of >= 1% in less than 10% of samples.

```{r prev_p}
t1 <- data.table(Phylum = ra_p$Phylum,
                 `Number of Samples` = rowSums(ra_p[, 2:ncol(ra_p)] >= 0.01))
t1$`Percent Samples` <-  t1$`Number of Samples`/72

setorder(t1, -`Number of Samples`)
datatable(t1,
          rownames = FALSE,
          caption = "Taxonomic  count table",
          class = "cell-border stripe",
          options = list(search = FALSE,
                         pageLength = nrow(t1))) %>%
  formatPercentage(columns = 3,
                   digits = 1)
```

We will remove Chlamydiae from this analysis.

```{r keep_6_phyla, warning=FALSE,echo=FALSE,message=FALSE}
keep_p <- t1$Phylum[t1$`Percent Samples` >= 0.1]
# # Keep all
# keep_p <- t1$Phylum

paste0(keep_p, collapse = ", ")

ps1 <- subset_taxa(ps0, 
                   Phylum %in% keep_p )
otu1 <- data.table(ps1@tax_table@.Data,
                   t(ps1@otu_table@.Data))

datatable(head(otu1, 10),
          rownames = FALSE,
          caption = "Taxonomic  count table",
          class = "cell-border stripe",
          options = list(search = FALSE,
                         pageLength = 10)) %>%
  formatCurrency(columns = 7:ncol(otu1),
                 currency = "",
                 mark = ",",
                 digits = 0)
```

XX OTUs, down from YY OTUs in the previous table.


# Relative Abundance in Samples at Different Taxonomic Ranks
## 1. Class
```{r counts_c, warning=FALSE,echo=FALSE,message=FALSE,fig.width=15,fig.height=15}
counts_c <- counts_by_tax_rank(dt1 = otu1,
                               aggr_by = "Class")
ra_c <- ra_by_tax_rank(counts_c)

tax.ranks <- unique(otu1[, c("Phylum",
                             "Class")])

ra_c <- merge(tax.ranks,
              ra_c,
              by = "Class")

total <- rowSums(ra_c[, 3:ncol(ra_c)])

ra_c$Class <- factor(ra_c$Class,
                     levels = ra_c$Class[order(total)])

ra_c$Phylum <- factor(ra_c$Phylum,
                      levels = unique(ra_c$Phylum[order(total)]))
tmp <- melt.data.table(data = ra_c,
                       id.vars = 1:2,
                       measure.vars = 3:ncol(counts_c),
                       variable.name = "SAMPLE_NAME",
                       value.name = "RA")

tmp <- merge(data.table(SAMPLE_NAME = smpl$SAMPLE_NAME,
                        WEEK = smpl$WEEK,
                        TREATMENT = smpl$TREATMENT,
                        Genotype = smpl$Genotype),
             tmp,
             by = "SAMPLE_NAME")

# Plot samples
p1 <- ggplot(tmp,
             aes(x = SAMPLE_NAME,
                 y = RA,
                 fill = Class,
                 color = Phylum)) +
  facet_wrap(~ WEEK + TREATMENT + Genotype,
             scales = "free_x",
             nrow = 3) +
  geom_bar(stat = "identity") +
  scale_x_discrete("") +
  scale_y_continuous(expand = c(0, 0)) +
  theme(axis.text.x = element_text(angle = 0,
                                   hjust = 1))
ggplotly(p1)
```

```{r means_c, echo = FALSE, warning = FALSE, message = FALSE}
lra <- ra_melt(ra = ra_c,
               samples = smpl,
               sample_name = "SAMPLE_NAME")

mu <- data.table(aggregate(lra$RA,
                           by = list(Week = lra$WEEK,
                                     Treatment = lra$TREATMENT,
                                     Genotype = lra$Genotype,
                                     Class = lra$Class),
                           FUN = "mean"))
mu[, total := sum(x),
   by = "Class"]
ul <- unique(mu[, c("Class", 
                    "total")])
ul <- ul[order(total),]
mu$Class <- factor(mu$Class,
                   level = ul$Class)
mu$total <- NULL

datatable(mu,
          rownames = FALSE,
          caption = "Taxonomic  count table",
          class = "cell-border stripe",
          options = list(search = FALSE,
                         pageLength = 10,
                         order = list(list(3, 'desc')))) %>%
  formatCurrency(columns = 5,
                 currency = "",
                 mark = ",",
                 digits = 2)
```


```{r means_c_p0, fig.width = 9, fig.height = 7}
mu$Trt_Genotype <- factor(paste(mu$Treatment,
                          mu$Genotype,
                          sep = "_"))

p0 <- ggplot(mu,
             aes(x = Week,
                 y = x,
                 group = Trt_Genotype)) +
  facet_wrap(~ Class,
             scale = "free_y") +
  geom_line(position = position_dodge(0.3)) +
  geom_point(aes(fill = Trt_Genotype),
             shape = 21,
             size = 2,
             alpha = 0.5,
             position = position_dodge(0.3)) +
  scale_x_discrete("") +
  scale_y_continuous("Relative Abundance (%)") +
  theme(legend.position = "top",
        axis.text.x = element_text(angle = 45,
                                   hjust = 1))

tiff(filename = "tmp/wt_class_over_time.tiff",
     height = 5,
     width = 7,
     units = "in",
     res = 600,
     compression = "lzw+p")
print(p0)
graphics.off()

print(p0)
```


```{r means_c_p1, fig.height = 5, fig.width = 9}
p1 <- ggplot(mu,
             aes(x = x,
                 y = Class,
                 color = Trt_Genotype,
                 shape = Week)) +
  geom_point(size = 3,
             alpha = 0.5) +
  geom_vline(xintercept = 1,
             linetype = "dashed") +
  scale_x_continuous("Relative Abundance (%)") +
  theme(legend.position = "top")

tiff(filename = "tmp/wt_class_ra.tiff",
     height = 4,
     width = 7,
     units = "in",
     res = 600,
     compression = "lzw+p")
print(p1)
graphics.off()

ggplotly(p1)
```

## 2. Order
```{r counts_o, warning=FALSE,echo=FALSE,message=FALSE,fig.width=15,fig.height=15}
counts_o <- counts_by_tax_rank(dt1 = otu1,
                               aggr_by = "Order")
ra_o <- ra_by_tax_rank(counts_o)

tax.ranks <- unique(otu1[, c("Phylum",
                             "Order")])

ra_o <- merge(tax.ranks,
              ra_o,
              by = "Order")

total <- rowSums(ra_o[, 3:ncol(ra_o)])

ra_o$Order <- factor(ra_o$Order,
                     levels = ra_o$Order[order(total)])

ra_o$Phylum <- factor(ra_o$Phylum,
                      levels = unique(ra_o$Phylum[order(total)]))
tmp <- melt.data.table(data = ra_o,
                       id.vars = 1:2,
                       measure.vars = 3:ncol(counts_o),
                       variable.name = "SAMPLE_NAME",
                       value.name = "RA")

tmp <- merge(data.table(SAMPLE_NAME = smpl$SAMPLE_NAME,
                        WEEK = smpl$WEEK,
                        TREATMENT = smpl$TREATMENT,
                        Genotype = smpl$Genotype),
             tmp,
             by = "SAMPLE_NAME")

# Plot samples
p1 <- ggplot(tmp,
             aes(x = SAMPLE_NAME,
                 y = RA,
                 fill = Order,
                 color = Phylum)) +
  facet_wrap(~ WEEK + TREATMENT + Genotype,
             scales = "free_x",
             nrow = 3) +
  geom_bar(stat = "identity") +
  scale_x_discrete("") +
  scale_y_continuous(expand = c(0, 0)) +
  theme(axis.text.x = element_text(angle = 0,
                                   hjust = 1))
ggplotly(p1)
```

```{r means_o, echo = FALSE, warning = FALSE, message = FALSE}
lra <- ra_melt(ra = ra_o,
               samples = smpl,
               sample_name = "SAMPLE_NAME")

mu <- data.table(aggregate(lra$RA,
                           by = list(Week = lra$WEEK,
                                     Treatment = lra$TREATMENT,
                                     Genotype = lra$Genotype,
                                     Order = lra$Order),
                           FUN = "mean"))
mu[, total := sum(x),
   by = "Order"]
ul <- unique(mu[, c("Order", 
                    "total")])
ul <- ul[order(total),]
mu$Order <- factor(mu$Order,
                   level = ul$Order)
mu$total <- NULL

datatable(mu,
          rownames = FALSE,
          caption = "Taxonomic  count table",
          class = "cell-border stripe",
          options = list(search = FALSE,
                         pageLength = 10,
                         order = list(list(3, 'desc')))) %>%
  formatCurrency(columns = 5,
                 currency = "",
                 mark = ",",
                 digits = 2)
```

```{r means_o_p0, fig.width = 9, fig.height = 7}
mu$Trt_Genotype <- factor(paste(mu$Treatment,
                                mu$Genotype,
                                sep = "_"))

p0 <- ggplot(mu,
             aes(x = Week,
                 y = x,
                 group = Trt_Genotype)) +
  facet_wrap(~ Order,
             scale = "free_y") +
  geom_line(position = position_dodge(0.3)) +
  geom_point(aes(fill = Trt_Genotype),
             shape = 21,
             size = 2,
             alpha = 0.5,
             position = position_dodge(0.3)) +
  scale_x_discrete("") +
  scale_y_continuous("Relative Abundance (%)") +
  theme(legend.position = "top",
        axis.text.x = element_text(angle = 45,
                                   hjust = 1))

tiff(filename = "tmp/wt_Order_over_time.tiff",
     height = 5,
     width = 7,
     units = "in",
     res = 600,
     compression = "lzw+p")
print(p0)
graphics.off()

print(p0)
```

```{r means_o_p1, fig.height = 5, fig.width = 9}
p1 <- ggplot(mu,
             aes(x = x,
                 y = Order,
                 color = Trt_Genotype,
                 shape = Week)) +
  geom_point(size = 3,
             alpha = 0.5) +
  geom_vline(xintercept = 1,
             linetype = "dashed") +
  scale_x_continuous("Relative Abundance (%)") +
  theme(legend.position = "top")

tiff(filename = "tmp/wt_Order_ra.tiff",
     height = 4,
     width = 7,
     units = "in",
     res = 600,
     compression = "lzw+p")
print(p1)
graphics.off()

ggplotly(p1)
```

## 3. Family
```{r counts_f, warning=FALSE,echo=FALSE,message=FALSE,fig.width=15,fig.height=15}
counts_f <- counts_by_tax_rank(dt1 = otu1,
                               aggr_by = "Family")
ra_f <- ra_by_tax_rank(counts_f)

tax.ranks <- unique(otu1[, c("Phylum",
                             "Family")])

ra_f <- merge(tax.ranks,
              ra_f,
              by = "Family")

total <- rowSums(ra_f[, 3:ncol(ra_f)])

ra_f$Family <- factor(ra_f$Family,
                     levels = ra_f$Family[order(total)])

ra_f$Phylum <- factor(ra_f$Phylum,
                      levels = unique(ra_f$Phylum[order(total)]))
tmp <- melt.data.table(data = ra_f,
                       id.vars = 1:2,
                       measure.vars = 3:ncol(counts_f),
                       variable.name = "SAMPLE_NAME",
                       value.name = "RA")

tmp <- merge(data.table(SAMPLE_NAME = smpl$SAMPLE_NAME,
                        WEEK = smpl$WEEK,
                        TREATMENT = smpl$TREATMENT,
                        Genotype = smpl$Genotype),
             tmp,
             by = "SAMPLE_NAME")

# Plot samples
p1 <- ggplot(tmp,
             aes(x = SAMPLE_NAME,
                 y = RA,
                 fill = Family,
                 color = Phylum)) +
  facet_wrap(~ WEEK + TREATMENT + Genotype,
             scales = "free_x",
             nrow = 3) +
  geom_bar(stat = "identity") +
  scale_x_discrete("") +
  scale_y_continuous(expand = c(0, 0)) +
  theme(axis.text.x = element_text(angle = 0,
                                   hjust = 1))
ggplotly(p1)
```

```{r means_f, echo = FALSE, warning = FALSE, message = FALSE}
lra <- ra_melt(ra = ra_f,
               samples = smpl,
               sample_name = "SAMPLE_NAME")

mu <- data.table(aggregate(lra$RA,
                           by = list(Week = lra$WEEK,
                                     Treatment = lra$TREATMENT,
                                     Genotype = lra$Genotype,
                                     Family = lra$Family),
                           FUN = "mean"))
mu[, total := sum(x),
   by = "Family"]
ul <- unique(mu[, c("Family", 
                    "total")])
ul <- ul[order(total),]
mu$Family <- factor(mu$Family,
                   level = ul$Family)
mu$total <- NULL

datatable(mu,
          rownames = FALSE,
          caption = "Taxonomic  count table",
          class = "cell-border stripe",
          options = list(search = FALSE,
                         pageLength = 10,
                         Family = list(list(3, 'desc')))) %>%
  formatCurrency(columns = 5,
                 currency = "",
                 mark = ",",
                 digits = 2)
```

NOTE: only the first 24 families had large enough counts - ploting only them.  
  
```{r means_f_p0, fig.width = 9, fig.height = 7}
mu$Trt_Genotype <- factor(paste(mu$Treatment,
                                mu$Genotype,
                                sep = "_"))
mu1 <- droplevels(mu[Family %in% levels(mu$Family)[nlevels(mu$Family):(nlevels(mu$Family) - 24)], ])

p0 <- ggplot(mu1,
             aes(x = Week,
                 y = x,
                 group = Trt_Genotype)) +
  facet_wrap(~ Family,
             scale = "free_y") +
  geom_line(position = position_dodge(0.3)) +
  geom_point(aes(fill = Trt_Genotype),
             shape = 21,
             size = 2,
             alpha = 0.5,
             position = position_dodge(0.3)) +
  scale_x_discrete("") +
  scale_y_continuous("Relative Abundance (%)") +
  theme(legend.position = "top",
        axis.text.x = element_text(angle = 45,
                                   hjust = 1))

tiff(filename = "tmp/wt_Family_over_time.tiff",
     height = 7,
     width = 9,
     units = "in",
     res = 600,
     compression = "lzw+p")
print(p0)
graphics.off()

print(p0)
```

```{r means_f_p1, fig.height = 5, fig.width = 9}
p1 <- ggplot(mu1,
             aes(x = x,
                 y = Family,
                 color = Trt_Genotype,
                 shape = Week)) +
  geom_point(size = 3,
             alpha = 0.5) +
  geom_vline(xintercept = 1,
             linetype = "dashed") +
  scale_x_continuous("Relative Abundance (%)") +
  theme(legend.position = "top")

tiff(filename = "tmp/wt_Family_ra.tiff",
     height = 4,
     width = 7,
     units = "in",
     res = 600,
     compression = "lzw+p")
print(p1)
graphics.off()

ggplotly(p1)
```

## 4. Genus
```{r counts_g, warning=FALSE,echo=FALSE,message=FALSE,fig.width=15,fig.height=15}
counts_g <- counts_by_tax_rank(dt1 = otu1,
                               aggr_by = "Genus")
ra_g <- ra_by_tax_rank(counts_g)

tax.ranks <- unique(otu1[, c("Phylum",
                             "Genus")])

ra_g <- merge(tax.ranks,
              ra_g,
              by = "Genus")

total <- rowSums(ra_g[, 3:ncol(ra_g)])

ra_g$Genus <- factor(ra_g$Genus,
                     levels = ra_g$Genus[order(total)])

ra_g$Phylum <- factor(ra_g$Phylum,
                      levels = unique(ra_g$Phylum[order(total)]))
tmp <- melt.data.table(data = ra_g,
                       id.vars = 1:2,
                       measure.vars = 3:ncol(counts_g),
                       variable.name = "SAMPLE_NAME",
                       value.name = "RA")

tmp <- merge(data.table(SAMPLE_NAME = smpl$SAMPLE_NAME,
                        WEEK = smpl$WEEK,
                        TREATMENT = smpl$TREATMENT,
                        Genotype = smpl$Genotype),
             tmp,
             by = "SAMPLE_NAME")

# Plot samples
p1 <- ggplot(tmp,
             aes(x = SAMPLE_NAME,
                 y = RA,
                 fill = Genus,
                 color = Phylum)) +
  facet_wrap(~ WEEK + TREATMENT + Genotype,
             scales = "free_x",
             nrow = 3) +
  geom_bar(stat = "identity") +
  scale_x_discrete("") +
  scale_y_continuous(expand = c(0, 0)) +
  theme(axis.text.x = element_text(angle = 0,
                                   hjust = 1))
ggplotly(p1)
```


```{r means_g, echo = FALSE, warning = FALSE, message = FALSE}
lra <- ra_melt(ra = ra_g,
               samples = smpl,
               sample_name = "SAMPLE_NAME")

mu <- data.table(aggregate(lra$RA,
                           by = list(Week = lra$WEEK,
                                     Treatment = lra$TREATMENT,
                                     Genotype = lra$Genotype,
                                     Genus = lra$Genus),
                           FUN = "mean"))
mu[, total := sum(x),
   by = "Genus"]
ul <- unique(mu[, c("Genus", 
                    "total")])
ul <- ul[order(total),]
mu$Genus <- factor(mu$Genus,
                   level = ul$Genus)
mu$total <- NULL

datatable(mu,
          rownames = FALSE,
          caption = "Taxonomic  count table",
          class = "cell-border stripe",
          options = list(search = FALSE,
                         pageLength = 10,
                         Genus = list(list(3, 'desc')))) %>%
  formatCurrency(columns = 5,
                 currency = "",
                 mark = ",",
                 digits = 2)
```

```{r means_g_p0, fig.width = 9, fig.height = 7}
mu$Trt_Genotype <- factor(paste(mu$Treatment,
                                mu$Genotype,
                                sep = "_"))
mu1 <- droplevels(mu[Genus %in% levels(mu$Genus)[nlevels(mu$Genus):(nlevels(mu$Genus) - 35)], ])

p0 <- ggplot(mu1,
             aes(x = Week,
                 y = x,
                 group = Trt_Genotype)) +
  facet_wrap(~ Genus,
             scale = "free_y") +
  geom_line(position = position_dodge(0.3)) +
  geom_point(aes(fill = Trt_Genotype),
             shape = 21,
             size = 2,
             alpha = 0.5,
             position = position_dodge(0.3)) +
  scale_x_discrete("") +
  scale_y_continuous("Relative Abundance (%)") +
  theme(legend.position = "top",
        axis.text.x = element_text(angle = 45,
                                   hjust = 1))

tiff(filename = "tmp/wt_Genus_over_time.tiff",
     height = 9,
     width = 12,
     units = "in",
     res = 600,
     compression = "lzw+p")
print(p0)
graphics.off()

print(p0)
```

```{r means_g_p1, fig.height = 9, fig.width = 9}
p1 <- ggplot(mu1,
             aes(x = x,
                 y = Genus,
                 color = Trt_Genotype,
                 shape = Week)) +
  geom_point(size = 3,
             alpha = 0.5) +
  geom_vline(xintercept = 1,
             linetype = "dashed") +
  scale_x_continuous("Relative Abundance (%)") +
  theme(legend.position = "top")

tiff(filename = "tmp/wt_Genus_ra.tiff",
     height = 9,
     width = 9,
     units = "in",
     res = 600,
     compression = "lzw+p")
print(p1)
graphics.off()

ggplotly(p1)
```



# Session Information
```{r info,eval=TRUE}
sessionInfo()
```